This study investigates the factors that affect the user’s intention to use dashcam in Malaysia. This study examines the quantitative relationship of intrinsic as well as extrinsic factors such as personal innovativeness, perceived uniqueness, perceived usefulness, perceived ease of use, attitude, perceived behavioral control, social influence, price value, and trust to the purchase intention of dashcam. Purposive sampling technique was employed to collect responses from 232 respondents based on two criteria: first, individuals who have experienced driving on the road and have a car; and second, individuals who not yet adopt or purchase the dashcam. The data were analyzed using SmartPLS (version 3.3.2). No relationship between personal innovativeness and perceived usefulness was found, in contrast to a significant relationship the former and perceived ease of use. Furthermore, perceived uniqueness was found significant to both perceived usefulness and perceived ease of use. Consistent with the literature, both perceived usefulness and perceived ease of use were identified as factors influencing attitude. However, perceived usefulness did not affect intention. Perceived behavioral control, social influence, attitude, and trust significantly affected the behavioral intention to use the dashcam in Malaysia. This study attempts to integrate and adapt two technology adoption models, namely the Combined Technology Acceptance Model and Theory Planned Behavior and extension of Unified Theory of Acceptance and Use of Technology, also extends the model with personal innovativeness, perceived uniqueness, and trust to fulfil the study’s objectives as well.

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References

Abdallah, A., Dwivedi, Y. K. and Rana, N. P. [2017] International Journal of Information Management Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37, 3: 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002 Crossref, Google Scholar
Agarwal, N. and Brem, A. [2015] Strategic business transformation through technology convergence: Implications from General Electric’s industrial internet initiative. International Journal of Technology Management, 67, 2/3/4: 196. https://doi.org/10.1504/IJTM.2015.068224 Crossref, Google Scholar
Agrebi, S. and Jallais, J. [2015] Journal of Retailing and Consumer Services Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22: 16–23. https://doi.org/10.1016/j.jretconser.2014.09.003 Crossref, Google Scholar
Ajzen, I. [1985] From intentions to actions: A theory of planned behavior. Action Control. Springer, Berlin, Heidelberg, pp. 11–39. https://doi.org/10.1007/978-3-642-69746-3_2. Crossref, Google Scholar
Al-Debei, M. M. and Al-Lozi, E. [2014] Explaining and predicting the adoption intention of mobile data services: A value-based approach. Computers in Human Behavior, 35: 326–338. https://doi.org/10.1016/j.chb.2014.03.011 Crossref, Google Scholar
Aldossari, M. Q., Sidorova, A. and Aldossari, M. Q. [2020] Consumer acceptance of internet of things (IoT): Smart home context consumer acceptance of internet of things (IoT): Smart home context. Journal of Computer Information Systems, 60: 6, 507–517. https://doi.org/10.1080/08874417.2018.1543000 Crossref, Google Scholar
Asare, A. K., Brashear-Alejandro, T. G. and Kang, J. [2016] B2B technology adoption in customer driven supply chains. Journal of Business and Industrial Marketing, 31, 1: 1–12. https://doi.org/10.1108/JBIM-02-2015-0022 Crossref, Google Scholar
Ayuning Budi, N. F., Adnan, H. R., Firmansyah, F., Hidayanto, A. N., Kurnia, S. and Purwandari, B. [2021] Why do people want to use location-based application for emergency situations? The extension of UTAUT perspectives. Technology in Society, 65: 101480. https://doi.org/10.1016/j.techsoc.2020.101480 Crossref, Google Scholar
Baabdullah, A. M. [2018] Technology in society consumer adoption of mobile social network games (M-SNGs) in Saudi Arabia: The role of social influence, hedonic motivation and trust. Technology in Society, 53: 91–102. https://doi.org/10.1016/j.techsoc.2018.01.004 Crossref, Google Scholar
Bashir, I. and Madhavaiah, C. [2015] Consumer attitude and behavioural intention towards Internet banking adoption in India. Journal of Indian Business Research, 7, 1: 67–102. https://doi.org/10.1108/JIBR-02-2014-0013 Crossref, Google Scholar
Beh, P. K., Ganesan, Y., Iranmanesh, M. and Foroughi, B. [2021] Using smartwatches for fitness and health monitoring: The UTAUT2 combined with threat appraisal as moderators. Behaviour & Information Technology, 40, 3: 282–299. https://doi.org/10.1080/0144929X.2019.1685597 Crossref, Google Scholar
Brem, A. and Viardot, É. [2015] Adoption of innovation: Balancing internal and external stakeholders in the marketing of innovation. In Adoption of Innovation. Springer International Publishing, New York, pp. 1–10. https://doi.org/10.1007/978-3-319-14523-5_1. Crossref, Google Scholar
Carvajal-trujillo, E. and Bons, E. [2015] Influence of trust and perceived value on the intention to purchase travel online: Integrating the effects of assurance on trust antecedents. Tourism Management, 47: 286–302. https://doi.org/10.1016/j.tourman.2014.10.009 Crossref, Google Scholar
Chen, C.-L. and Wu, C.-C. [2020] Students’ behavioral intention to use and achievements in ICT-Integrated mathematics remedial instruction: Case study of a calculus course. Computers & Education, 145: 103740. https://doi.org/10.1016/j.compedu.2019.103740 Crossref, Google Scholar
Chen, H. H. and Chen, S. C. [2009] The empirical study of automotive telematics acceptance in Taiwan: Comparing three technology acceptance models. International Journal of Mobile Communications, 7, 1: 50–65. https://doi.org/10.1504/IJMC.2009.021672 Crossref, Google Scholar
Chen, N. H. [2019] Extending a TAM–TTF model with perceptions toward telematics adoption. Asia Pacific Journal of Marketing and Logistics, 31, 1: 37–54. https://doi.org/10.1108/APJML-02-2018-0074 Crossref, Google Scholar
Cheng, E. W. L. [2019] Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM). Educational Technology Research and Development, 67, 1: 21–37. https://doi.org/10.1007/s11423-018-9598-6 Crossref, Google Scholar
Chin, W. W. [2010] How to write up and report PLS analyses. Handbook of Partial Least Squares. Springer, Berlin, pp. 655–690. https://doi.org/10.1007/978-3-540-32827-8_29. Crossref, Google Scholar
Dai, J., Li, R. and Liu, Z. [2021] Does initial experience affect consumers’ intention to use autonomous vehicles? Evidence from a field experiment in Beijing. Accident Analysis & Prevention, 149: 105778. https://doi.org/10.1016/j.aap.2020.105778 Crossref, Google Scholar
Djeni and Erbilek, M. [2018] Intention to use biometrie systems among international students in Cyprus. 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN), Cyprus, 2017, 10.1109/CICN.2017.8319391. Google Scholar
Dutot, V., Bhatiasevi, V. and Bellallahom, N. [2019] Applying the technology acceptance model in a three-countries study of smartwatch adoption. Journal of High Technology Management Research, 30, 1: 1–14. https://doi.org/10.1016/j.hitech.2019.02.001 Crossref, Google Scholar
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M. and Williams, M. D. [2019] Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21, 3: 719–734. https://doi.org/10.1007/s10796-017-9774-y Crossref, Google Scholar
Edmunds, R., Dorn, L. and Skrypchuk, L. [2014] The profile of emotional designs: A tool for the measurement of affective and cognitive responses to in-vehicle innovations. Driver Acceptance of New Technology: Theory, Measurement and Optimisation, CRC Press, Boca Raton, pp. 105–120. https://doi.org/10.1201/9781315578132-8 Google Scholar
Ertz, M., Karakas, F. and Sarigöllü, E. [2016] Exploring pro-environmental behaviors of consumers: An analysis of contextual factors, attitude, and behaviors. Journal of Business Research, 69, 10: 3971–3980. https://doi.org/10.1016/j.jbusres.2016.06.010 Crossref, Google Scholar
Foroughi, B., Iranmanesh, M. and Hyun, S. S. [2019] Understanding the determinants of mobile banking continuance usage intention. Journal of Enterprise Information Management, 32, 6: 1015–1033. https://doi.org/10.1108/JEIM-10-2018-0237 Crossref, Google Scholar
Gershon, P., Sita, K. R., Zhu, C., Ehsani, J. P., Klauer, S. G., Dingus, T. A. and Simons-Morton, B. G. [2019] Distracted driving, visual inattention, and crash risk among teenage drivers. American Journal of Preventive Medicine, 56, 4: 494–500. https://doi.org/10.1016/j.amepre.2018.11.024 Crossref, Google Scholar
Goodhue, D. L. [1995] Understanding user evaluations of information systems. Management Science, 41, 12: 1827–1844. https://doi.org/10.1287/mnsc.41.12.1827 Crossref, Google Scholar
Hair, E., Halle, T., Terry-Humen, E., Lavelle, B. and Calkins, J. [2006] Children’s school readiness in the ECLS-K: Predictions to academic, health, and social outcomes in first grade. Early Childhood Research Quarterly, 21, 4: 431–454. https://doi.org/10.1016/j.ecresq.2006.09.005 Crossref, Google Scholar
Hair, J. F., Ringle, C. M. and Sarstedt, M. [2011] PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19, 2: 139–152. https://doi.org/10.2753/MTP1069-6679190202 Crossref, Google Scholar
Hair, J., Hollingsworth, C. L., Randolph, A. B. and Chong, A. Y. L. [2017] An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management and Data Systems, 117, 3: 442–458. https://doi.org/10.1108/IMDS-04-2016-0130 Crossref, Google Scholar
Henseler, J., Ringle, C. M. and Sarstedt, M. [2014] A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 1: 115–135. https://doi.org/10.1007/s11747-014-0403-8 Crossref, Google Scholar
Holdack, E., Lurie-Stoyanov, K. and Fromme, H. F. [2020] The role of perceived enjoyment and perceived informativeness in assessing the acceptance of AR wearables. Journal of Retailing and Consumer Services, 102259. https://doi.org/10.1016/j.jretconser.2020.102259 Crossref, Google Scholar
Huang, X. and Ge, J. [2019] Electric vehicle development in Beijing: An analysis of consumer purchase intention. Journal of Cleaner Production, 216: 361–372. https://doi.org/10.1016/j.jclepro.2019.01.231 Crossref, Google Scholar
Hutchinson, T. E. [2004] Driving Confidence and In-Vehicle Telematics: A Study of Technology Adoption Patterns of the 50 + Driving Population. Massachusetts Institute of Technology, Cambridge, MA. Google Scholar
Huynh, T. L. D., Vo, A. K. H., Nguyen, T. H. H., Nguyen, V. B., Le, Ho, N. N. H. and Do, N. B. [2020] What makes us use the shared mobility model? Evidence from Vietnam. Economic Analysis and Policy, 66: 1–13. https://doi.org/10.1016/j.eap.2020.02.007 Crossref, Google Scholar
Jeon, H. M., Sung, H. J. and Kim, H. Y. [2020] Customers’ acceptance intention of self-service technology of restaurant industry: Expanding UTAUT with perceived risk and innovativeness. Service Business, 14, 4: 533–551. https://doi.org/10.1007/s11628-020-00425-6 Crossref, Google Scholar
Kadu, S., Cheggoju, N. and Satpute, V. R. [2018] Noise-resilient compressed domain video watermarking system for in-car camera security. Multimedia Systems, 24, 5: 583–595. https://doi.org/10.1007/s00530-017-0584-3 Crossref, Google Scholar
Karaali, D., Altin, C. and Calisir, F. [2011] Computers in Human Behavior Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Computers in Human Behavior, 27, 1: 343–354. https://doi.org/10.1016/j.chb.2010.08.012 Crossref, Google Scholar
Kasilingam, D. L. [2020] Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62: 101280. https://doi.org/10.1016/j.techsoc.2020.101280 Crossref, Google Scholar
Kaur, K. and Rampersad, G. [2018] Trust in driverless cars: Investigating key factors influencing the adoption of driverless cars. Journal of Engineering and Technology Management, 48: 87–96. https://doi.org/10.1016/j.jengtecman.2018.04.006 Crossref, Google Scholar
Kim, C., Takashima, K. and Newell, S. [2018] How do retailers increase the benefits of buyer innovativeness? Asia Pacific Journal of Marketing and Logistics, 30, 3: 571–586. https://doi.org/10.1108/APJML-03-2017-0043 Crossref, Google Scholar
Kim, K. J. and Shin, D. H. [2015] An acceptance model for smart watches: Implications for the adoption of future wearable technology. Internet Research, 25, 4: 527–541. https://doi.org/10.1108/IntR-05-2014-0126 Crossref, Google Scholar
Kim, Y. and Kim, Y. [2017] A technology-fashion collaborative product: Its impact on consumer attitudes and purchase intention A technology-fashion collaborative product: Its impact on consumer attitudes and purchase intention. Journal of Global Fashion Marketing, 2685, August: 1–15. https://doi.org/10.1080/20932685.2017.1348240 Google Scholar
Lu, Y., Papagiannidis, S. and Alamanos, E. [2019] Exploring the emotional antecedents and outcomes of technology acceptance. Computers in Human Behavior, 90: 153–169. https://doi.org/10.1016/j.chb.2018.08.056 Crossref, Google Scholar
Lynn, M. and Harris, J. [1997] The desire for unique consumer prooducts. Psychology & Marketing, 14, 6: 601–616. Crossref, Google Scholar
Ma, Q., Chan, A. H. S. and Chen, K. [2016] Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Applied Ergonomics, 54: 62–71. https://doi.org/10.1016/j.apergo.2015.11.015 Crossref, Google Scholar
Mcknight, D. H., Carter, M., Thatcher, J. B. and Clay, P. F. [2011] Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems, 2, 2: 1–25. https://doi.org/10.1145/1985347.1985353 Crossref, Google Scholar
Mensah, I. K. [2018] E-government services adoption: The important elements of trust and transparency. International Journal of Electronic Government Research, 14, 3: 12–31. https://doi.org/10.4018/IJEGR.2018070102 Crossref, Google Scholar
Modak, A., Paradkar S., Manwatkar S., Madane A. R., Deshpande A. M. [2020] Human head pose and eye state based driver distraction monitoring system, In Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, Vol. 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_34. Crossref, Google Scholar
Moehrle, M. G. and Caferoglu, H. [2019] Technological speciation as a source for emerging technologies. Using semantic patent analysis for the case of camera technology. Technological Forecasting and Social Change, 146, November 2017: 776–784. https://doi.org/10.1016/j.techfore.2018.07.049 Crossref, Google Scholar
Nedra, B.-A., Hadhri, W. and Mezrani, M. [2019] Determinants of customers’ intentions to use hedonic networks: The case of Instagram. Journal of Retailing and Consumer Services, 46: 21–32. https://doi.org/10.1016/j.jretconser.2018.09.001 Crossref, Google Scholar
NST. (2020). Malaysia’s Vehicle Sales Expected at 610,000 Units in 2020. New Straits Times, Malaysia. Google Scholar
Osswald, S., Wurhofer, D., Trösterer, S., Beck, E. and Tscheligi, M. [2012] Predicting information technology usage in the car: Towards a car technology acceptance model. In Automotive UI 2012—4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, In-Cooperation with ACM SIGCHI—Proceedings, Portsmouth, New Hampshire, pp. 51–58. https://doi.org/10.1145/2390256.2390264 Crossref, Google Scholar
Park, E., Kim, H. and Ohm, J. Y. [2015] Understanding driver adoption of car navigation systems using the extended technology acceptance model. Behaviour and Information Technology, 34, 7: 741–751. https://doi.org/10.1080/0144929X.2014.963672 Crossref, Google Scholar
Park, E. and Kim, K. J. [2014] Driver acceptance of car navigation systems: Integration of locational accuracy, processing speed, and service and display quality with technology acceptance model. Personal and Ubiquitous Computing, 18, 3: 503–513. https://doi.org/10.1007/s00779-013-0670-2 Crossref, Google Scholar
Patil, P., Tamilmani, K., Rana, N. P. and Raghavan, V. [2020] Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54: 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144 Crossref, Google Scholar
Paul, W., Pascal, B., Erik, K., Sebastian, B. and Jurgen, B. [2017] Privacy Dashcam—towards lawful use of dashcams through enforcement of external anonymization. International Workshop on Data Privacy Management, 2: 183–201. https://doi.org/10.1007/978-3-319-67816-0 Google Scholar
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y. and Podsakoff, N. P. [2003] Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 5: 879–903. https://doi.org/10.1037/0021-9010.88.5.879 Crossref, Google Scholar
Podsakoff, P. M. and Organ, D. W. [1986] Self-reports in organizational research: Problems and prospects. Journal of Management, 12, 4: 531–544. https://doi.org/10.1177/014920638601200408 Crossref, Google Scholar
Ramírez-Correa, P., Rondán-Cataluña, F. J., Arenas-Gaitán, J. and Martín-Velicia, F. [2019] Analysing the acceptation of online games in mobile devices: An application of UTAUT2. Journal of Retailing and Consumer Services, 50, 85–93. https://doi.org/10.1016/j.jretconser.2019.04.018 Crossref, Google Scholar
Rea, R. V., Johnson, C. J., Aitken, D. A., Child, K. N. and Hesse, G. [2018] Dash Cam videos on YouTube^TM offer insights into factors related to moose-vehicle collisions. Accident Analysis and Prevention, 118, September: 207–213. https://doi.org/10.1016/j.aap.2018.02.020 Crossref, Google Scholar
Ringle, C. M., Wende, S. and Becker, J.-M. (2015). SmartPLS 3. (3.3.2). Available at http://www.smartpls.com. Google Scholar
Sener, I. N., Zmud, J. and Williams, T. [2019] Measures of baseline intent to use automated vehicles: A case study of Texas cities. Transportation Research Part F: Psychology and Behaviour, 62: 66–77. https://doi.org/10.1016/j.trf.2018.12.014 Crossref, Google Scholar
Shaikh, H., Khan, M. S., Mahar, Z. A., Anwar, M., Raza, A. and Shah, A. [2019] A conceptual framework for determining acceptance of internet of things (IoT) in higher education institutions of Pakistan. In 2019 International Conference on Information Science and Communication Technology, ICISCT 2019, Piscataway, New Jersey, pp. 1–5. https://doi.org/10.1109/CISCT.2019.8777431 Crossref, Google Scholar
Sharififard, S., Tamam, E., Hj Hassan, M. S., Waheed, M. and Zaremohzzabieh, Z. [2016] Factors affecting Malaysian university students’ purchase intention in social networking sites. Cogent Business and Management, 3, 1: 1–12. https://doi.org/10.1080/23311975.2016.1182612 Google Scholar
Shiau, W. L. and Chau, P. Y. K. [2016] Understanding behavioral intention to use a cloud computing classroom: A multiple model comparison approach. Information and Management, 53, 3: 355–365. https://doi.org/10.1016/j.im.2015.10.004 Crossref, Google Scholar
Shin, D. H. [2009] Determinants of customer acceptance of multi-service network: An implication for IP-based technologies. Information and Management, 46, 1: 16–22. https://doi.org/10.1016/j.im.2008.05.004 Crossref, Google Scholar
Singh, N. and Sinha, N. [2020] How perceived trust mediates merchant’s intention to use a mobile wallet technology. Journal of Retailing and Consumer Services, 52, 101894. https://doi.org/10.1016/j.jretconser.2019.101894 Crossref, Google Scholar
Slade, E., Williams, M., Dwivedi, Y. and Piercy, N. [2015] Exploring consumer adoption of proximity mobile payments. Journal of Strategic Marketing, 23, 3: 209–223. https://doi.org/10.1080/0965254X.2014.914075. Crossref, Google Scholar
Štitilis, D. and Laurinaitis, M. [2016] Legal regulation of the use of dashboard cameras: Aspects of privacy protection. Computer Law & Security Review: The International Journal of Technology Law and Practice, 32, 2: 316–326. https://doi.org/10.1016/j.clsr.2016.01.012 Crossref, Google Scholar
Syuhaidi, M., Bakar, A. and Bidin, R. [2014] Technology acceptance and purchase intention towards movie mobile advertising among youth in Malaysia. Procedia—Social and Behavioral Sciences, 130: 558–567. https://doi.org/10.1016/j.sbspro.2014.04.065 Crossref, Google Scholar
Taylor, S. and Todd, P. A. [1995] Understanding information technology usage: A test of competing models. Information Systems Research, 6, 2: 144–176. https://doi.org/10.1287/isre.6.2.144 Crossref, Google Scholar
TheStar. (2019). We have the third highest death rate from road accidents, https://www.thestar.com.my/lifestyle/health/2019/05/14/we-have-the-third-highest-death-rate-from-road-accidents. Google Scholar
TheStar. (2020). More fatal road accidents. Available at https://www.thestar.com.my/news/nation/2020/02/06/more-fatal-road-accidents. Google Scholar
TheSunDaily. (2019). Miros thumbs-up for dashboard cameras. Available at https://www.thesundaily.my/local/miros-thumbs-up-for-dashboard-cameras-BA516074. Google Scholar
Tummala, G. K., Das, T., Sinha, P. and Ramnath, R. [2019] SmartDashCam: Automatic live calibration for dashcams. In IPSN 2019—Proceedings of the 2019 Information Processing in Sensor Networks, Montreal, pp. 157–168. https://doi.org/10.1145/3302506.3310397 Crossref, Google Scholar
Vafaei-Zadeh, A., Ramayah, T., Hanifah, H., Kurnia, S. and Mahmud, I. [2020] Supply chain information integration and its impact on the operational performance of manufacturing firms in Malaysia. Information and Management, 57, 8: 103386. https://doi.org/10.1016/j.im.2020.103386 Crossref, Google Scholar
Vafaei-Zadeh, A., Thurasamy, R. and Hanifah, H. [2019] Modeling anti-malware use intention of university students in a developing country using the theory of planned behavior. Kybernetes, 48, 8: 1565–1585. https://doi.org/10.1108/K-05-2018-0226 Crossref, Google Scholar
Venkatesh, Thong and Xu. [2012] Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36, 1: 157. https://doi.org/10.2307/41410412 Crossref, Google Scholar
Venkatesh, V. [2000] Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11, 4: 342–365. https://doi.org/10.1287/isre.11.4.342.11872 Crossref, Google Scholar
Wang, N., Tiberius, V., Chen, X., Brem, A. and Yu, F. [2020] Idea selection and adoption by users—a process model in an online innovation community. Technology Analysis & Strategic Management, 1–16. https://doi.org/10.1080/09537325.2020.1863055 Crossref, Google Scholar
Wang, Q., Zhang, W., Tseng, C. P. M.-L., Sun, Y. and Zhang, Y. [2021] Intention in use recyclable express packaging in consumers’ behavior: An empirical study. Resources, Conservation and Recycling, 164: 105115. https://doi.org/10.1016/j.resconrec.2020.105115 Crossref, Google Scholar
Wu, L., Wu, L. and Chang, S. [2016] Computers in human behavior exploring consumers’ intention to accept smartwatch. Computers in Human Behavior, 64: 383–392. https://doi.org/10.1016/j.chb.2016.07.005 Crossref, Google Scholar
Xin, H., Techatassanasoontorn, A. A. and Tan, F. B. [2015] Antecedents of consumer trust in mobile payment adoption. Journal of Computer Information Systems, 55, 4: pp. 1–10. https://doi.org/10.1080/08874417.2015.11645781 Crossref, Google Scholar
Yang, Y. and Wang, X. [2019] Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model. Computers and Education, 133: 116–126. https://doi.org/10.1016/j.compedu.2019.01.015 Crossref, Google Scholar
Yi, M. Y., Fiedler, K. D. and Park, J. S. [2006] Understanding the role of individual innovativeness in the acceptance of IT-based innovations: Comparative analyses of models and measures. Decision Sciences, 37, 3: 393–426. https://doi.org/10.1111/j.1540-5414.2006.00132.x Crossref, Google Scholar
Zhang, E. M. [2010] Understanding the acceptance of mobile SMS advertising among young chinese consumers. Psychology & Marketing, 30, 6: 461–469. https://doi.org/10.1002/mar Google Scholar
Zheng, J. and Li, S. [2020] What drives students’ intention to use tablet computers: An extended technology acceptance model. International Journal of Educational Research, 102: 101612. https://doi.org/10.1016/j.ijer.2020.101612 Crossref, Google Scholar